Overview

Dataset statistics

Number of variables22
Number of observations1910
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.4 KiB
Average record size in memory176.1 B

Variable types

Numeric19
Categorical3

Alerts

Data has a high cardinality: 1910 distinct valuesHigh cardinality
Val15 has a high cardinality: 1775 distinct valuesHigh cardinality
Val14 has a high cardinality: 1211 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with ConcursoHigh correlation
Concurso is highly overall correlated with Unnamed: 0High correlation
G15_Num is highly overall correlated with G14_numHigh correlation
G14_num is highly overall correlated with G15_NumHigh correlation
Concurso is uniformly distributedUniform
Data is uniformly distributedUniform
Val14 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Concurso has unique valuesUnique
Data has unique valuesUnique
G15_Num has 136 (7.1%) zerosZeros

Reproduction

Analysis started2023-03-09 13:43:11.030643
Analysis finished2023-03-09 13:43:36.895307
Duration25.86 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1910
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3983.5194
Minimum0
Maximum7772
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:36.944305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile387.45
Q11954.75
median4151
Q36007.75
95-th percentile7416.65
Maximum7772
Range7772
Interquartile range (IQR)4053

Descriptive statistics

Standard deviation2294.3412
Coefficient of variation (CV)0.57595834
Kurtosis-1.2787314
Mean3983.5194
Median Absolute Deviation (MAD)2003
Skewness-0.087435614
Sum7608522
Variance5264001.5
MonotonicityStrictly increasing
2023-03-09T10:43:37.111335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
5475 1
 
0.1%
5507 1
 
0.1%
5504 1
 
0.1%
5501 1
 
0.1%
5499 1
 
0.1%
5497 1
 
0.1%
5494 1
 
0.1%
5488 1
 
0.1%
5485 1
 
0.1%
Other values (1900) 1900
99.5%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
4 1
0.1%
5 1
0.1%
7 1
0.1%
9 1
0.1%
10 1
0.1%
11 1
0.1%
14 1
0.1%
ValueCountFrequency (%)
7772 1
0.1%
7771 1
0.1%
7768 1
0.1%
7764 1
0.1%
7761 1
0.1%
7759 1
0.1%
7758 1
0.1%
7757 1
0.1%
7754 1
0.1%
7752 1
0.1%

Concurso
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1910
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean955.5
Minimum1
Maximum1910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:37.192510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1478.25
median955.5
Q31432.75
95-th percentile1814.55
Maximum1910
Range1909
Interquartile range (IQR)954.5

Descriptive statistics

Standard deviation551.51383
Coefficient of variation (CV)0.57719919
Kurtosis-1.2
Mean955.5
Median Absolute Deviation (MAD)477.5
Skewness0
Sum1825005
Variance304167.5
MonotonicityStrictly increasing
2023-03-09T10:43:37.267590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1270 1
 
0.1%
1282 1
 
0.1%
1281 1
 
0.1%
1280 1
 
0.1%
1279 1
 
0.1%
1278 1
 
0.1%
1277 1
 
0.1%
1276 1
 
0.1%
1275 1
 
0.1%
Other values (1900) 1900
99.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1910 1
0.1%
1909 1
0.1%
1908 1
0.1%
1907 1
0.1%
1906 1
0.1%
1905 1
0.1%
1904 1
0.1%
1903 1
0.1%
1902 1
0.1%
1901 1
0.1%

Data
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1910
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
29/09/2003
 
1
13/10/2015
 
1
09/11/2015
 
1
06/11/2015
 
1
04/11/2015
 
1
Other values (1905)
1905 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters19100
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1910 ?
Unique (%)100.0%

Sample

1st row29/09/2003
2nd row06/10/2003
3rd row13/10/2003
4th row20/10/2003
5th row27/10/2003

Common Values

ValueCountFrequency (%)
29/09/2003 1
 
0.1%
13/10/2015 1
 
0.1%
09/11/2015 1
 
0.1%
06/11/2015 1
 
0.1%
04/11/2015 1
 
0.1%
03/11/2015 1
 
0.1%
30/10/2015 1
 
0.1%
28/10/2015 1
 
0.1%
26/10/2015 1
 
0.1%
23/10/2015 1
 
0.1%
Other values (1900) 1900
99.5%

Length

2023-03-09T10:43:37.339284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29/09/2003 1
 
0.1%
05/01/2004 1
 
0.1%
20/10/2003 1
 
0.1%
27/10/2003 1
 
0.1%
03/11/2003 1
 
0.1%
10/11/2003 1
 
0.1%
17/11/2003 1
 
0.1%
24/11/2003 1
 
0.1%
01/12/2003 1
 
0.1%
08/12/2003 1
 
0.1%
Other values (1900) 1900
99.5%

Most occurring characters

ValueCountFrequency (%)
0 4817
25.2%
/ 3820
20.0%
1 3199
16.7%
2 3187
16.7%
3 611
 
3.2%
8 609
 
3.2%
7 603
 
3.2%
9 587
 
3.1%
6 572
 
3.0%
5 548
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15280
80.0%
Other Punctuation 3820
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4817
31.5%
1 3199
20.9%
2 3187
20.9%
3 611
 
4.0%
8 609
 
4.0%
7 603
 
3.9%
9 587
 
3.8%
6 572
 
3.7%
5 548
 
3.6%
4 547
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/ 3820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4817
25.2%
/ 3820
20.0%
1 3199
16.7%
2 3187
16.7%
3 611
 
3.2%
8 609
 
3.2%
7 603
 
3.2%
9 587
 
3.1%
6 572
 
3.0%
5 548
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4817
25.2%
/ 3820
20.0%
1 3199
16.7%
2 3187
16.7%
3 611
 
3.2%
8 609
 
3.2%
7 603
 
3.2%
9 587
 
3.1%
6 572
 
3.0%
5 548
 
2.9%

Bola1
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.195288
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:37.394802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q320
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.2928762
Coefficient of variation (CV)0.55268792
Kurtosis-1.2043324
Mean13.195288
Median Absolute Deviation (MAD)6
Skewness-0.029852188
Sum25203
Variance53.186044
MonotonicityNot monotonic
2023-03-09T10:43:37.456865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 95
 
5.0%
11 92
 
4.8%
25 88
 
4.6%
23 87
 
4.6%
18 86
 
4.5%
16 83
 
4.3%
22 82
 
4.3%
10 81
 
4.2%
15 78
 
4.1%
24 78
 
4.1%
Other values (15) 1060
55.5%
ValueCountFrequency (%)
1 95
5.0%
2 58
3.0%
3 73
3.8%
4 75
3.9%
5 70
3.7%
6 70
3.7%
7 74
3.9%
8 77
4.0%
9 73
3.8%
10 81
4.2%
ValueCountFrequency (%)
25 88
4.6%
24 78
4.1%
23 87
4.6%
22 82
4.3%
21 77
4.0%
20 69
3.6%
19 76
4.0%
18 86
4.5%
17 58
3.0%
16 83
4.3%

Bola2
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.734555
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:37.517443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2380904
Coefficient of variation (CV)0.56838189
Kurtosis-1.199826
Mean12.734555
Median Absolute Deviation (MAD)6
Skewness0.03367272
Sum24323
Variance52.389953
MonotonicityNot monotonic
2023-03-09T10:43:37.579073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
7 93
 
4.9%
15 92
 
4.8%
19 87
 
4.6%
1 87
 
4.6%
3 86
 
4.5%
2 85
 
4.5%
9 85
 
4.5%
22 79
 
4.1%
10 78
 
4.1%
24 78
 
4.1%
Other values (15) 1060
55.5%
ValueCountFrequency (%)
1 87
4.6%
2 85
4.5%
3 86
4.5%
4 74
3.9%
5 61
3.2%
6 78
4.1%
7 93
4.9%
8 71
3.7%
9 85
4.5%
10 78
4.1%
ValueCountFrequency (%)
25 72
3.8%
24 78
4.1%
23 62
3.2%
22 79
4.1%
21 75
3.9%
20 66
3.5%
19 87
4.6%
18 64
3.4%
17 75
3.9%
16 73
3.8%

Bola3
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.034555
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:37.639106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2073866
Coefficient of variation (CV)0.55294459
Kurtosis-1.1828492
Mean13.034555
Median Absolute Deviation (MAD)6
Skewness0.0014172926
Sum24896
Variance51.946422
MonotonicityNot monotonic
2023-03-09T10:43:37.700933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
12 92
 
4.8%
20 90
 
4.7%
13 89
 
4.7%
11 86
 
4.5%
14 84
 
4.4%
25 82
 
4.3%
3 80
 
4.2%
10 79
 
4.1%
5 79
 
4.1%
1 78
 
4.1%
Other values (15) 1071
56.1%
ValueCountFrequency (%)
1 78
4.1%
2 73
3.8%
3 80
4.2%
4 75
3.9%
5 79
4.1%
6 65
3.4%
7 73
3.8%
8 67
3.5%
9 75
3.9%
10 79
4.1%
ValueCountFrequency (%)
25 82
4.3%
24 75
3.9%
23 77
4.0%
22 72
3.8%
21 78
4.1%
20 90
4.7%
19 68
3.6%
18 75
3.9%
17 68
3.6%
16 61
3.2%

Bola4
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.112565
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:37.761347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.196734
Coefficient of variation (CV)0.54884256
Kurtosis-1.1973947
Mean13.112565
Median Absolute Deviation (MAD)6
Skewness-0.018312339
Sum25045
Variance51.79298
MonotonicityNot monotonic
2023-03-09T10:43:37.823159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
17 91
 
4.8%
5 90
 
4.7%
19 88
 
4.6%
4 88
 
4.6%
16 83
 
4.3%
24 83
 
4.3%
7 83
 
4.3%
15 83
 
4.3%
25 82
 
4.3%
12 81
 
4.2%
Other values (15) 1058
55.4%
ValueCountFrequency (%)
1 73
3.8%
2 67
3.5%
3 70
3.7%
4 88
4.6%
5 90
4.7%
6 69
3.6%
7 83
4.3%
8 68
3.6%
9 68
3.6%
10 66
3.5%
ValueCountFrequency (%)
25 82
4.3%
24 83
4.3%
23 70
3.7%
22 74
3.9%
21 69
3.6%
20 74
3.9%
19 88
4.6%
18 65
3.4%
17 91
4.8%
16 83
4.3%

Bola5
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.961257
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:37.884746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2445544
Coefficient of variation (CV)0.55893921
Kurtosis-1.2153993
Mean12.961257
Median Absolute Deviation (MAD)6
Skewness0.020170687
Sum24756
Variance52.483569
MonotonicityNot monotonic
2023-03-09T10:43:37.945776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 96
 
5.0%
20 89
 
4.7%
6 89
 
4.7%
25 85
 
4.5%
5 85
 
4.5%
17 82
 
4.3%
14 82
 
4.3%
19 80
 
4.2%
16 79
 
4.1%
1 77
 
4.0%
Other values (15) 1066
55.8%
ValueCountFrequency (%)
1 77
4.0%
2 75
3.9%
3 73
3.8%
4 74
3.9%
5 85
4.5%
6 89
4.7%
7 70
3.7%
8 96
5.0%
9 68
3.6%
10 64
3.4%
ValueCountFrequency (%)
25 85
4.5%
24 77
4.0%
23 73
3.8%
22 69
3.6%
21 66
3.5%
20 89
4.7%
19 80
4.2%
18 68
3.6%
17 82
4.3%
16 79
4.1%

Bola6
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.186911
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:38.009356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2612072
Coefficient of variation (CV)0.55063746
Kurtosis-1.1932635
Mean13.186911
Median Absolute Deviation (MAD)6
Skewness-0.031755629
Sum25187
Variance52.72513
MonotonicityNot monotonic
2023-03-09T10:43:38.070941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11 94
 
4.9%
17 91
 
4.8%
24 90
 
4.7%
15 89
 
4.7%
3 85
 
4.5%
25 85
 
4.5%
13 81
 
4.2%
1 78
 
4.1%
5 78
 
4.1%
18 78
 
4.1%
Other values (15) 1061
55.5%
ValueCountFrequency (%)
1 78
4.1%
2 71
3.7%
3 85
4.5%
4 71
3.7%
5 78
4.1%
6 68
3.6%
7 66
3.5%
8 73
3.8%
9 67
3.5%
10 69
3.6%
ValueCountFrequency (%)
25 85
4.5%
24 90
4.7%
23 76
4.0%
22 74
3.9%
21 76
4.0%
20 71
3.7%
19 69
3.6%
18 78
4.1%
17 91
4.8%
16 66
3.5%

Bola7
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.936649
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:38.130976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1551443
Coefficient of variation (CV)0.553091
Kurtosis-1.1917326
Mean12.936649
Median Absolute Deviation (MAD)6
Skewness-0.024954145
Sum24709
Variance51.196089
MonotonicityNot monotonic
2023-03-09T10:43:38.192524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3 88
 
4.6%
14 87
 
4.6%
16 85
 
4.5%
18 85
 
4.5%
13 83
 
4.3%
22 80
 
4.2%
12 80
 
4.2%
23 79
 
4.1%
6 79
 
4.1%
20 79
 
4.1%
Other values (15) 1085
56.8%
ValueCountFrequency (%)
1 79
4.1%
2 77
4.0%
3 88
4.6%
4 67
3.5%
5 76
4.0%
6 79
4.1%
7 69
3.6%
8 75
3.9%
9 77
4.0%
10 67
3.5%
ValueCountFrequency (%)
25 59
3.1%
24 76
4.0%
23 79
4.1%
22 80
4.2%
21 69
3.6%
20 79
4.1%
19 75
3.9%
18 85
4.5%
17 75
3.9%
16 85
4.5%

Bola8
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.945026
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:38.252559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1652363
Coefficient of variation (CV)0.5535127
Kurtosis-1.201655
Mean12.945026
Median Absolute Deviation (MAD)6
Skewness0.01446728
Sum24725
Variance51.340612
MonotonicityNot monotonic
2023-03-09T10:43:38.316406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
9 98
 
5.1%
13 89
 
4.7%
21 86
 
4.5%
5 83
 
4.3%
20 82
 
4.3%
2 81
 
4.2%
16 79
 
4.1%
23 78
 
4.1%
17 77
 
4.0%
24 77
 
4.0%
Other values (15) 1080
56.5%
ValueCountFrequency (%)
1 68
3.6%
2 81
4.2%
3 76
4.0%
4 77
4.0%
5 83
4.3%
6 75
3.9%
7 70
3.7%
8 73
3.8%
9 98
5.1%
10 76
4.0%
ValueCountFrequency (%)
25 69
3.6%
24 77
4.0%
23 78
4.1%
22 68
3.6%
21 86
4.5%
20 82
4.3%
19 67
3.5%
18 74
3.9%
17 77
4.0%
16 79
4.1%

Bola9
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.065969
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:38.377059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q320
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.2432005
Coefficient of variation (CV)0.55435619
Kurtosis-1.1787333
Mean13.065969
Median Absolute Deviation (MAD)6
Skewness-0.0056350056
Sum24956
Variance52.463954
MonotonicityNot monotonic
2023-03-09T10:43:38.440027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
20 103
 
5.4%
11 95
 
5.0%
1 91
 
4.8%
13 89
 
4.7%
9 87
 
4.6%
25 87
 
4.6%
24 84
 
4.4%
6 83
 
4.3%
21 82
 
4.3%
10 81
 
4.2%
Other values (15) 1028
53.8%
ValueCountFrequency (%)
1 91
4.8%
2 78
4.1%
3 60
3.1%
4 74
3.9%
5 61
3.2%
6 83
4.3%
7 64
3.4%
8 72
3.8%
9 87
4.6%
10 81
4.2%
ValueCountFrequency (%)
25 87
4.6%
24 84
4.4%
23 60
3.1%
22 72
3.8%
21 82
4.3%
20 103
5.4%
19 65
3.4%
18 77
4.0%
17 65
3.4%
16 53
2.8%

Bola10
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.706806
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:38.588236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.3394751
Coefficient of variation (CV)0.57760187
Kurtosis-1.247145
Mean12.706806
Median Absolute Deviation (MAD)6
Skewness0.03012752
Sum24270
Variance53.867895
MonotonicityNot monotonic
2023-03-09T10:43:38.649236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 97
 
5.1%
4 94
 
4.9%
3 92
 
4.8%
20 86
 
4.5%
19 82
 
4.3%
21 81
 
4.2%
7 81
 
4.2%
1 80
 
4.2%
12 78
 
4.1%
13 78
 
4.1%
Other values (15) 1061
55.5%
ValueCountFrequency (%)
1 80
4.2%
2 97
5.1%
3 92
4.8%
4 94
4.9%
5 71
3.7%
6 66
3.5%
7 81
4.2%
8 62
3.2%
9 74
3.9%
10 77
4.0%
ValueCountFrequency (%)
25 74
3.9%
24 77
4.0%
23 70
3.7%
22 64
3.4%
21 81
4.2%
20 86
4.5%
19 82
4.3%
18 72
3.8%
17 72
3.8%
16 72
3.8%

Bola11
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.246073
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:38.710851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median14
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1685663
Coefficient of variation (CV)0.54118425
Kurtosis-1.1911646
Mean13.246073
Median Absolute Deviation (MAD)6
Skewness-0.043629864
Sum25300
Variance51.388342
MonotonicityNot monotonic
2023-03-09T10:43:38.771468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6 91
 
4.8%
15 90
 
4.7%
19 87
 
4.6%
24 87
 
4.6%
22 87
 
4.6%
10 86
 
4.5%
14 85
 
4.5%
17 84
 
4.4%
7 79
 
4.1%
18 78
 
4.1%
Other values (15) 1056
55.3%
ValueCountFrequency (%)
1 70
3.7%
2 72
3.8%
3 76
4.0%
4 63
3.3%
5 66
3.5%
6 91
4.8%
7 79
4.1%
8 73
3.8%
9 67
3.5%
10 86
4.5%
ValueCountFrequency (%)
25 74
3.9%
24 87
4.6%
23 77
4.0%
22 87
4.6%
21 62
3.2%
20 77
4.0%
19 87
4.6%
18 78
4.1%
17 84
4.4%
16 72
3.8%

Bola12
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.056021
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:38.832372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2628927
Coefficient of variation (CV)0.55628684
Kurtosis-1.2244966
Mean13.056021
Median Absolute Deviation (MAD)6
Skewness-0.0069628986
Sum24937
Variance52.74961
MonotonicityNot monotonic
2023-03-09T10:43:38.894114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 92
 
4.8%
16 85
 
4.5%
24 83
 
4.3%
11 83
 
4.3%
5 82
 
4.3%
21 82
 
4.3%
23 81
 
4.2%
10 81
 
4.2%
12 80
 
4.2%
22 79
 
4.1%
Other values (15) 1082
56.6%
ValueCountFrequency (%)
1 74
3.9%
2 75
3.9%
3 74
3.9%
4 92
4.8%
5 82
4.3%
6 73
3.8%
7 66
3.5%
8 73
3.8%
9 67
3.5%
10 81
4.2%
ValueCountFrequency (%)
25 76
4.0%
24 83
4.3%
23 81
4.2%
22 79
4.1%
21 82
4.3%
20 65
3.4%
19 75
3.9%
18 76
4.0%
17 76
4.0%
16 85
4.5%

Bola13
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.933508
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:38.954122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1961428
Coefficient of variation (CV)0.55639529
Kurtosis-1.2058636
Mean12.933508
Median Absolute Deviation (MAD)6
Skewness0.026414344
Sum24703
Variance51.784471
MonotonicityNot monotonic
2023-03-09T10:43:39.015576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
14 93
 
4.9%
5 89
 
4.7%
4 89
 
4.7%
13 84
 
4.4%
18 82
 
4.3%
9 81
 
4.2%
23 80
 
4.2%
7 79
 
4.1%
3 79
 
4.1%
22 78
 
4.1%
Other values (15) 1076
56.3%
ValueCountFrequency (%)
1 73
3.8%
2 67
3.5%
3 79
4.1%
4 89
4.7%
5 89
4.7%
6 67
3.5%
7 79
4.1%
8 74
3.9%
9 81
4.2%
10 77
4.0%
ValueCountFrequency (%)
25 75
3.9%
24 75
3.9%
23 80
4.2%
22 78
4.1%
21 75
3.9%
20 71
3.7%
19 67
3.5%
18 82
4.3%
17 75
3.9%
16 67
3.5%

Bola14
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.856021
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:39.076225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1551469
Coefficient of variation (CV)0.55655999
Kurtosis-1.1694197
Mean12.856021
Median Absolute Deviation (MAD)6
Skewness0.0079869371
Sum24555
Variance51.196127
MonotonicityNot monotonic
2023-03-09T10:43:39.137224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 95
 
5.0%
10 94
 
4.9%
18 86
 
4.5%
23 85
 
4.5%
17 84
 
4.4%
15 82
 
4.3%
7 82
 
4.3%
12 81
 
4.2%
11 80
 
4.2%
5 79
 
4.1%
Other values (15) 1062
55.6%
ValueCountFrequency (%)
1 77
4.0%
2 95
5.0%
3 69
3.6%
4 69
3.6%
5 79
4.1%
6 68
3.6%
7 82
4.3%
8 69
3.6%
9 68
3.6%
10 94
4.9%
ValueCountFrequency (%)
25 70
3.7%
24 65
3.4%
23 85
4.5%
22 79
4.1%
21 70
3.7%
20 55
2.9%
19 75
3.9%
18 86
4.5%
17 84
4.4%
16 76
4.0%

Bola15
Real number (ℝ)

Distinct25
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.263351
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:39.197809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q320
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.2905191
Coefficient of variation (CV)0.549674
Kurtosis-1.2555454
Mean13.263351
Median Absolute Deviation (MAD)6.5
Skewness-0.044596938
Sum25333
Variance53.151668
MonotonicityNot monotonic
2023-03-09T10:43:39.260385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
21 92
 
4.8%
23 92
 
4.8%
10 90
 
4.7%
22 85
 
4.5%
20 85
 
4.5%
19 84
 
4.4%
2 84
 
4.4%
18 82
 
4.3%
12 80
 
4.2%
25 79
 
4.1%
Other values (15) 1057
55.3%
ValueCountFrequency (%)
1 65
3.4%
2 84
4.4%
3 75
3.9%
4 75
3.9%
5 77
4.0%
6 74
3.9%
7 73
3.8%
8 67
3.5%
9 72
3.8%
10 90
4.7%
ValueCountFrequency (%)
25 79
4.1%
24 72
3.8%
23 92
4.8%
22 85
4.5%
21 92
4.8%
20 85
4.5%
19 84
4.4%
18 82
4.3%
17 70
3.7%
16 63
3.3%

G15_Num
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0057592
Minimum0
Maximum94
Zeros136
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:39.328389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum94
Range94
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.2803508
Coefficient of variation (CV)1.3181898
Kurtosis71.430402
Mean4.0057592
Median Absolute Deviation (MAD)2
Skewness6.5331472
Sum7651
Variance27.882104
MonotonicityNot monotonic
2023-03-09T10:43:39.396008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2 377
19.7%
1 344
18.0%
3 295
15.4%
4 237
12.4%
5 149
 
7.8%
0 136
 
7.1%
6 97
 
5.1%
7 68
 
3.6%
8 61
 
3.2%
9 30
 
1.6%
Other values (31) 116
 
6.1%
ValueCountFrequency (%)
0 136
 
7.1%
1 344
18.0%
2 377
19.7%
3 295
15.4%
4 237
12.4%
5 149
 
7.8%
6 97
 
5.1%
7 68
 
3.6%
8 61
 
3.2%
9 30
 
1.6%
ValueCountFrequency (%)
94 1
0.1%
66 1
0.1%
51 1
0.1%
50 1
0.1%
47 1
0.1%
43 1
0.1%
41 1
0.1%
39 2
0.1%
37 1
0.1%
36 2
0.1%

G14_num
Real number (ℝ)

Distinct797
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean658.4534
Minimum127
Maximum27396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2023-03-09T10:43:39.467080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum127
5-th percentile285
Q1389.5
median510
Q3686
95-th percentile1362.55
Maximum27396
Range27269
Interquartile range (IQR)296.5

Descriptive statistics

Standard deviation858.47957
Coefficient of variation (CV)1.3037818
Kurtosis511.18243
Mean658.4534
Median Absolute Deviation (MAD)141
Skewness18.376829
Sum1257646
Variance736987.17
MonotonicityNot monotonic
2023-03-09T10:43:39.545076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
525 10
 
0.5%
351 9
 
0.5%
319 9
 
0.5%
410 9
 
0.5%
415 9
 
0.5%
334 9
 
0.5%
337 8
 
0.4%
464 8
 
0.4%
456 8
 
0.4%
484 8
 
0.4%
Other values (787) 1823
95.4%
ValueCountFrequency (%)
127 1
0.1%
151 1
0.1%
154 1
0.1%
158 1
0.1%
180 1
0.1%
184 1
0.1%
189 1
0.1%
193 1
0.1%
194 1
0.1%
196 1
0.1%
ValueCountFrequency (%)
27396 1
0.1%
9354 1
0.1%
9040 1
0.1%
7687 1
0.1%
7488 1
0.1%
7312 1
0.1%
5889 1
0.1%
5589 1
0.1%
5503 1
0.1%
4551 1
0.1%

Val15
Categorical

Distinct1775
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
0
 
136
49.765,82
 
1
742.189,80
 
1
303.052,99
 
1
936.238,41
 
1
Other values (1770)
1770 

Length

Max length12
Median length10
Mean length9.8073298
Min length1

Characters and Unicode

Total characters18732
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1774 ?
Unique (%)92.9%

Sample

1st row49.765,82
2nd row596.323,70
3rd row400.623,70
4th row902.226,02
5th row380.017,55

Common Values

ValueCountFrequency (%)
0 136
 
7.1%
49.765,82 1
 
0.1%
742.189,80 1
 
0.1%
303.052,99 1
 
0.1%
936.238,41 1
 
0.1%
2.122.133,58 1
 
0.1%
270.747,41 1
 
0.1%
877.825,08 1
 
0.1%
821.651,87 1
 
0.1%
1.827.729,94 1
 
0.1%
Other values (1765) 1765
92.4%

Length

2023-03-09T10:43:39.615693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 136
 
7.1%
487.887,61 1
 
0.1%
380.017,55 1
 
0.1%
489.140,06 1
 
0.1%
104.625,29 1
 
0.1%
1.201.958,08 1
 
0.1%
336.053,65 1
 
0.1%
3.490.966,18 1
 
0.1%
269.157,25 1
 
0.1%
411.362,89 1
 
0.1%
Other values (1765) 1765
92.4%

Most occurring characters

ValueCountFrequency (%)
. 2236
11.9%
1 1821
9.7%
, 1774
9.5%
2 1591
8.5%
0 1506
8.0%
3 1465
7.8%
5 1443
7.7%
4 1437
7.7%
6 1413
7.5%
7 1368
7.3%
Other values (2) 2678
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14722
78.6%
Other Punctuation 4010
 
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1821
12.4%
2 1591
10.8%
0 1506
10.2%
3 1465
10.0%
5 1443
9.8%
4 1437
9.8%
6 1413
9.6%
7 1368
9.3%
8 1365
9.3%
9 1313
8.9%
Other Punctuation
ValueCountFrequency (%)
. 2236
55.8%
, 1774
44.2%

Most occurring scripts

ValueCountFrequency (%)
Common 18732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2236
11.9%
1 1821
9.7%
, 1774
9.5%
2 1591
8.5%
0 1506
8.0%
3 1465
7.8%
5 1443
7.7%
4 1437
7.7%
6 1413
7.5%
7 1368
7.3%
Other values (2) 2678
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2236
11.9%
1 1821
9.7%
, 1774
9.5%
2 1591
8.5%
0 1506
8.0%
3 1465
7.8%
5 1443
7.7%
4 1437
7.7%
6 1413
7.5%
7 1368
7.3%
Other values (2) 2678
14.3%

Val14
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1211
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
1.544,00
 
6
1.309,00
 
5
1.363,00
 
5
1.374,00
 
5
1.591,00
 
5
Other values (1206)
1884 

Length

Max length8
Median length8
Mean length6.6806283
Min length2

Characters and Unicode

Total characters12760
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique728 ?
Unique (%)38.1%

Sample

1st row689
2nd row1.388,00
3rd row2.173,00
4th row1.498,00
5th row687

Common Values

ValueCountFrequency (%)
1.544,00 6
 
0.3%
1.309,00 5
 
0.3%
1.363,00 5
 
0.3%
1.374,00 5
 
0.3%
1.591,00 5
 
0.3%
1.696,00 5
 
0.3%
1.503,00 5
 
0.3%
1.759,00 5
 
0.3%
1.514,00 5
 
0.3%
1.559,00 5
 
0.3%
Other values (1201) 1859
97.3%

Length

2023-03-09T10:43:39.678307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1.544,00 6
 
0.3%
1.559,00 5
 
0.3%
1.309,00 5
 
0.3%
1.692,00 5
 
0.3%
1.449,00 5
 
0.3%
1.441,00 5
 
0.3%
1.846,00 5
 
0.3%
1.346,00 5
 
0.3%
1.429,00 5
 
0.3%
1.352,00 5
 
0.3%
Other values (1201) 1859
97.3%

Most occurring characters

ValueCountFrequency (%)
0 3361
26.3%
1 1711
13.4%
. 1407
11.0%
, 1407
11.0%
2 760
 
6.0%
3 665
 
5.2%
4 624
 
4.9%
6 591
 
4.6%
9 589
 
4.6%
5 575
 
4.5%
Other values (2) 1070
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9946
77.9%
Other Punctuation 2814
 
22.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3361
33.8%
1 1711
17.2%
2 760
 
7.6%
3 665
 
6.7%
4 624
 
6.3%
6 591
 
5.9%
9 589
 
5.9%
5 575
 
5.8%
8 574
 
5.8%
7 496
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 1407
50.0%
, 1407
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3361
26.3%
1 1711
13.4%
. 1407
11.0%
, 1407
11.0%
2 760
 
6.0%
3 665
 
5.2%
4 624
 
4.9%
6 591
 
4.6%
9 589
 
4.6%
5 575
 
4.5%
Other values (2) 1070
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3361
26.3%
1 1711
13.4%
. 1407
11.0%
, 1407
11.0%
2 760
 
6.0%
3 665
 
5.2%
4 624
 
4.9%
6 591
 
4.6%
9 589
 
4.6%
5 575
 
4.5%
Other values (2) 1070
 
8.4%

Interactions

2023-03-09T10:43:35.327982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.061444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.484160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.802304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.110486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.427501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.651392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.959498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.187559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.509718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.817750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.040739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.342190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.646687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.862714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.173719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.491927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.720952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.029747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.398501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.152683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.559261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.871933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.180091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.495129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.722422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.028623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.343220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.579271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.889296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.109865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.413787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.717491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.933755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.243891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.562647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.790395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.100954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.471414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.226683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.634263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.942965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.250652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.565629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.791677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.101700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.411795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.650043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.957882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.267743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.481992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.787144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.002330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.314484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.631233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.860431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.168538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.535416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.298200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.702774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.006316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.313684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.627593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.857460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.163963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.475016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.711570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.021921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.328743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.546182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.850141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.154958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.377099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.695844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.922938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.233549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.601438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.365830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.769572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.070553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.377272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.691174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.919466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.227996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.538021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.775372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.084497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.392364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.609254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.913721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.217788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.442110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.759363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.074110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.296110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.665946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.433678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.839627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.134684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.442313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.753784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.984008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.290522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.603724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.838328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.149851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.454406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.674138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.977337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.280360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.505138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.823941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.135112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.360127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.731952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.505941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.908149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.199277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.505129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.817831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.046885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.354095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.666292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.901947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.211403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.517879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.736120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.041376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.343360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.579234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.885527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.199687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.424236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.797143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.573587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.977213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.263890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.570405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.880429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.110757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.416067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.730329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.964563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.277474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.580952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.799691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.102950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.406942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.643275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.949576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.263136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.487863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.865149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.644063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.045675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.326887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.632487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.945483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.174343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.480652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.792510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.029553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.340449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.644556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.862244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.167829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.470120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.709243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.011779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.327418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.549819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.929665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-09T10:43:14.114673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.477026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.696209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.008045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.238347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.543700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.858095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.093082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-09T10:43:25.708064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-09T10:43:21.921095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.158917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.466760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.771720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.989859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.294157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.597737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.836857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.139392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.454218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.673978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:36.059184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.849826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.252811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.604041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.834814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.134971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.451231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.671309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.987685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.310524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.531759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.834687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.053869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.357155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.663284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.901371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.205914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.516804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.737966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:36.124250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:12.919830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.320812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.667690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.897395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.199584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.514276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.737317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.054260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.374141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.594342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.899356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-09T10:43:28.421713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.726801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.965952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.272475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.581415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.799866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:36.189607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-09T10:43:15.729690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.963037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.269079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.579467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.800831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.124922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.437175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.659305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:25.961960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.269456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.483301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.790284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.112132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.338478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.644418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.863913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:36.255645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.061772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.458015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.793284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.026064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.334088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.642474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.866342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.189316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.500749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.724305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.026913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.330458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.547367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.853338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.175288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.402212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.707618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:34.926492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:36.319919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.130775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.529730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.856866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.090690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.397696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.706053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.929349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.254840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.563378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.788887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.089535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.395079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.610947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.920834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.238796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.466564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.771641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.076601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:36.385086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.200904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.597242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.920869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.153265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.461313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.769640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:20.995859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.318839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.629582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.851785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.154168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.457154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.674748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:29.982892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.302410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.529565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.836602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.138280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:36.448116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.270466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.667756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:15.983448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.217297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.525280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.833673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.059374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.383495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.692196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.915335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.215708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.521666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.736928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.047969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.365034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.595122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.900129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.202793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:36.512236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:13.338502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:14.733761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:16.045394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:17.278929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:18.588763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:19.894868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:21.124002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:22.445147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:23.755743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:24.975745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:26.279225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:27.582228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:28.799515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:30.109099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:31.428035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:32.655103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:33.963202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-09T10:43:35.264468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-09T10:43:39.745310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Unnamed: 0ConcursoBola1Bola2Bola3Bola4Bola5Bola6Bola7Bola8Bola9Bola10Bola11Bola12Bola13Bola14Bola15G15_NumG14_num
Unnamed: 01.0001.0000.0070.026-0.0100.018-0.0160.016-0.0000.0160.0040.0190.014-0.0300.009-0.030-0.001-0.065-0.106
Concurso1.0001.0000.0070.026-0.0100.018-0.0160.016-0.0000.0160.0040.0190.014-0.0300.009-0.030-0.001-0.065-0.106
Bola10.0070.0071.000-0.012-0.027-0.058-0.0670.005-0.064-0.012-0.053-0.067-0.020-0.0280.023-0.060-0.035-0.036-0.062
Bola20.0260.026-0.0121.000-0.094-0.067-0.092-0.049-0.069-0.034-0.0300.013-0.053-0.064-0.076-0.0330.005-0.054-0.033
Bola3-0.010-0.010-0.027-0.0941.000-0.056-0.004-0.018-0.048-0.052-0.035-0.036-0.047-0.047-0.0620.022-0.0930.010-0.020
Bola40.0180.018-0.058-0.067-0.0561.000-0.003-0.071-0.032-0.034-0.024-0.033-0.044-0.050-0.033-0.101-0.0380.0270.012
Bola5-0.016-0.016-0.067-0.092-0.004-0.0031.000-0.060-0.0460.013-0.083-0.071-0.0620.002-0.054-0.073-0.0620.0240.028
Bola60.0160.0160.005-0.049-0.018-0.071-0.0601.000-0.036-0.059-0.057-0.018-0.015-0.069-0.004-0.046-0.0470.0070.012
Bola7-0.000-0.000-0.064-0.069-0.048-0.032-0.046-0.0361.000-0.089-0.019-0.044-0.045-0.038-0.0600.007-0.057-0.010-0.035
Bola80.0160.016-0.012-0.034-0.052-0.0340.013-0.059-0.0891.000-0.058-0.045-0.024-0.042-0.063-0.052-0.022-0.015-0.021
Bola90.0040.004-0.053-0.030-0.035-0.024-0.083-0.057-0.019-0.0581.000-0.104-0.004-0.053-0.048-0.049-0.037-0.015-0.040
Bola100.0190.019-0.0670.013-0.036-0.033-0.071-0.018-0.044-0.045-0.1041.000-0.025-0.030-0.060-0.020-0.043-0.004-0.008
Bola110.0140.014-0.020-0.053-0.047-0.044-0.062-0.015-0.045-0.024-0.004-0.0251.000-0.061-0.066-0.046-0.042-0.0040.015
Bola12-0.030-0.030-0.028-0.064-0.047-0.0500.002-0.069-0.038-0.042-0.053-0.030-0.0611.000-0.059-0.059-0.0420.0230.027
Bola130.0090.0090.023-0.076-0.062-0.033-0.054-0.004-0.060-0.063-0.048-0.060-0.066-0.0591.000-0.028-0.054-0.007-0.003
Bola14-0.030-0.030-0.060-0.0330.022-0.101-0.073-0.0460.007-0.052-0.049-0.020-0.046-0.059-0.0281.000-0.0430.0030.007
Bola15-0.001-0.001-0.0350.005-0.093-0.038-0.062-0.047-0.057-0.022-0.037-0.043-0.042-0.042-0.054-0.0431.0000.004-0.038
G15_Num-0.065-0.065-0.036-0.0540.0100.0270.0240.007-0.010-0.015-0.015-0.004-0.0040.023-0.0070.0030.0041.0000.625
G14_num-0.106-0.106-0.062-0.033-0.0200.0120.0280.012-0.035-0.021-0.040-0.0080.0150.027-0.0030.007-0.0380.6251.000

Missing values

2023-03-09T10:43:36.619498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-09T10:43:36.794481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0ConcursoDataBola1Bola2Bola3Bola4Bola5Bola6Bola7Bola8Bola9Bola10Bola11Bola12Bola13Bola14Bola15G15_NumG14_numVal15Val14
001.029/09/200318.020.025.023.010.011.024.014.06.02.013.09.05.016.03.05.0154.049.765,82689
112.006/10/200323.015.05.04.012.016.020.06.011.019.024.01.09.013.07.01.0184.0596.323,701.388,00
223.013/10/200320.023.012.08.06.01.07.011.014.04.016.010.09.017.024.02.0158.0400.623,702.173,00
344.020/10/200316.05.025.024.023.08.012.02.017.018.01.010.04.019.013.01.0258.0902.226,021.498,00
455.027/10/200315.013.020.02.011.024.09.016.04.023.025.012.08.019.01.02.0472.0380.017,55687
576.003/11/200323.019.01.05.07.021.016.010.015.025.06.02.012.04.017.02.0393.0489.140,061.066,00
697.010/11/200322.04.015.08.016.014.021.023.012.01.025.019.07.010.018.09.01333.0104.625,29301
7108.017/11/200319.016.018.09.013.08.05.025.017.010.06.015.01.022.020.01.0256.01.201.958,082.012,00
8119.024/11/200321.04.017.05.03.013.016.09.020.024.025.019.011.015.010.03.0407.0336.053,651.061,00
91410.001/12/200324.019.08.023.06.02.020.011.09.03.04.010.05.012.014.00.0380.001.157,00
Unnamed: 0ConcursoDataBola1Bola2Bola3Bola4Bola5Bola6Bola7Bola8Bola9Bola10Bola11Bola12Bola13Bola14Bola15G15_NumG14_numVal15Val14
190077521901.009/12/201917.05.010.09.019.015.013.018.04.020.014.016.02.03.012.02.0736.01.367.413,231.633,00
190177541902.011/12/201911.01.017.022.025.015.023.08.04.016.05.06.012.02.09.03.0509.0872.209,991.581,00
190277571903.013/12/201910.012.019.024.09.015.020.04.021.016.08.014.023.06.07.01.0315.02.956.190,962.887,00
190377581904.016/12/201925.08.013.010.07.012.015.011.014.018.023.017.06.01.020.01.0255.02.386.362,702.879,00
190477591905.018/12/201923.024.01.019.06.09.03.015.012.021.018.08.016.017.011.02.0596.01.188.538,261.753,00
190577611906.020/12/201918.04.014.017.025.06.010.03.016.011.023.01.024.07.02.03.0393.0932.249,542.189,00
190677641907.023/12/201913.04.021.09.05.010.019.03.017.01.016.023.024.015.025.04.0800.0408.154,57897
190777681908.026/12/201914.02.024.025.023.017.016.08.013.012.020.07.04.022.06.03.0444.0530.770,321.576,00
190877711909.028/12/20198.025.012.09.05.022.010.018.04.014.06.011.017.03.02.01.0338.02.130.630,601.939,00
190977721910.030/12/201922.03.09.014.015.04.016.02.07.018.010.023.013.025.012.03.0513.0565.404,781.453,00